METHODS EDUCATIONAL DATA MINING IN E-LEARNING SYSTEMS

In recent years, research on Educational Data Mining (EDM) has developed rapidly. However, most researches focus on data source issues, and ignore the importance of data preprocessing and data mining algorithms. This paper has studied EDM, with a special focus on educational big data mining algorithms. Firstly, it analyzed the relevant elements of EDM and introduces big data technology based on the requirements of educational data application. Then it introduced the common educational big data mining algorithms and their applications, and finally discussed the development trend of educational big data mining algorithms.

The way we learn has changed as a result of advances in science and technology and the growing influence of information technology on daily life. E-learning is a relatively new concept in educational settings, but it has progressively earned a spot in modern training techniques. Data mining methods are used in many applications, particularly in elearning. Comparing direct communications difficult to track the student involvement in online courses. To overcome the above mentioned issues the existing methods are analyzed various techniques of data mining and optimization methods which can be applied to bring out hidden knowledge from the educational data? In the e-learning work, preprocessing, feature selection and classification process are performed to improve the Kalboard 360 educational dataset accuracy significantly. This study focused on educational data mining (EDM) and employed decision trees, support vector machines (SVM), multilayer perceptrons, and the naive bayesian C4.5 algorithm among other classification techniques to assess the features for the prediction of students’ behaviour and academic achievement. This paper analyzes the advantages and shortcomings of each algorithm applied to data set on the basis of accuracy for various classification techniques like Multilayer Perceptron based ANN algorithm (MP-ANN), Decision Tree (DT) C4.5 and Hybrid Regression model with Multi Label Classification (HR-MLC) approach. The experimental result shows that the HR-MLC algorithm provides better performance in terms of higher accuracy, precision, recall and f-measure rather than the other existing methods.

Educational data mining (EDM) is an up-coming interdisciplinary research field, in which data mining (DM) techniques applying in educational data. Its objective is to better understand how students gain knowledge and recognize the settings in which they learn to improve educational outcomes. Educational systems can store a huge amount of data that coming from multiple sources in different formats. Each particular educational problem requires different types of the mining techniques because traditional DM techniques cannot be applied directly to these types of data and problems. There are many general data mining tools are available but these are not designed for deal with educational data and an educator cannot use these tools without knowledge of data mining concepts. To overcome from this problem many authors provided educational data mining tools with different data mining techniques for different purposes. This paper surveys the EDM Tools from 2001 to 2016 and presents the consolidated list of tools in one place to help the EDM researchers. Then categorizes the tools based on data mining methods and explains the usage of EDM Tools.

The article explores data mining methods that allow us to get helpful information from the data. The possibility of using these methods in practice in the financial sector was considered. Since financial activity is closely related to our social life, the use of data mining methods plays an essential role in the analysis and forecasting of the financial market in the modern era of big data. However, due to differences in the experience of researchers in different disciplines, it is not easy to use data mining methods when analyzing financial data. Therefore, creating a methodological basis for the practical application of data mining methods in the analysis of financial data is an urgent issue. The purpose of this article is to create a methodological basis for using data mining methods for efficient trading. When processing product data, a priori methods and visualization methods were used, and their implementation in practice was described. As a result, scenarios of computer applications were created as a sample of the practical implementation of the algorithms of these methods. Building a quantitative trading strategy requires first statistical analysis of the information in the market and then testing the quantitative model on the collected data. This study developed a quantitative trading system based on data mining methods. The primary development tool used is the Jupyter web platform, and three cores have been developed: quantitative data selection, strategy testing on data, time series analysis, and visualization. The developed system supports modules for making simple trading decisions.

Recent advances in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for large-scale knowledge discovery from huge database. Data mining (DM) technology has emerged as a means of performing this discovery. There are countless researchers working on designing efficient data mining techniques, methods, and algorithms. Many data mining methods and algorithms have been developed and applied in a lot of application fields [1]. Unfortunately, most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining [2]. In this talk, some basic issues of data mining are addressed. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? What is the rule we should obey in a data mining process? Through analyzing existing data mining methods, and domain-driven (or user-driven) data mining models [3-5], we find that we should take a data mining process as a process of knowledge transformation. Based on this understanding of data mining, a conceptual data mining model of domain-oriented data-driven data mining (3DM) is proposed [2]. The relationship between traditional domain-driven (or user-driven) data mining models and our proposed 3DM model is also analyzed. Some domain-oriented data-driven data mining algorithms for mining such knowledge as default rule [6], decision tree [7], and concept lattice [8] from database are proposed. The experiment results for these algorithms are also shown to illustrate the efficiency and performance of the knowledge acquired by our 3DM data mining algorithms.

Although learning management systems (LMS) have been widely adopted by higher educational institutions in many countries, they are considered an emerging technology in Saudi Arabia. Furthermore, research has demonstrated that the students’ use of them is not always satisfactory. This quantitative study investigated the factors that affect the students use of LMS in higher education by extending the technology acceptance model (TAM) and adapting eight external variables. Based on the probability multi-stage cluster sampling technique, online surveys were sent by email to 2000 students registered in three public universities in Saudi Arabia. 851 responses were submitted by participants, and 833 responses were used for data analysis. Using Partial Least Squares Structural Equations Modeling (PLS-SEM), the results revealed that perceived ease of use is affected by six factors (content quality, system navigation, ease of access, system interactivity, instructional assessment and system learnability). The findings confirmed that perceived usefulness has five determinants (content quality, learning support, system interactivity, instructional assessment and perceived ease of use). This research is relevant to researchers, decision makers and e-learning systems designers working to enhance students’ use of e-learning systems in higher education, in particular where there is not yet widespread adoption.

Digital education technology has become an essential component of the educational process and has greatly impacted the learning experience in schools and higher education. Especially, in recent years, there has been a surge in the adoption of educational applications and the use of e-learning platforms resulting in a vast amount of educational data being generated. Using Learning Analytics and Educational Data Mining can assist in extracting valuable information from educational data, which can enhance the learning experience for both learners and educators. However, there is a growing need to address privacy concerns and ensure data security, and thus research focus has shifted to the development of privacy frameworks and principles to ensure data privacy and security. This paper highlights the significance of data protection and privacy regulations, such as GDPR, in utilizing data mining methods in educational environments aiming on establishing the foundations for the creation of a privacy-focused framework for data processing within the educational platform.

The research analyzes the potential of AI and the prospects of using data mining in the educational process of universities. The authors show that students can apply tools and methods of educational data mining to solve tasks across various disciplines. This positively impacts the development of knowledge and skills in advanced methods of experimental data processing. The research identifies potential challenges in training and retraining educational personnel to study and apply educational data mining. These challenges include a lack of knowledge in mathematical disciplines and information technologies, the need to improve training programs necessary for mastering educational data mining, a shortage of instructors ready to support this discipline, and an insufficiently developed innovative thinking style among students. The authors developed and tested digital educational content and assignments for students on using AI and big data analysis in education. The research outlines the prospects for further study in teaching students to apply data mining.

The paper focuses on the degree of influence of Big Data on building the educational process at the Russian Law University during the COVID-19 pandemic. We use the sociological method as the primary research method. The result shows the influence of the collected information on administrative decision-making. Additionally, we describe the educational process within the selected university. The paper demonstrates student attitude toward Big Data in various aspects, including awareness of the technology and its significance for the university's management. Weconclude that Big Data has not yet been widely used in Russian higher education. Therefore, we identify the reasons preventing the use of Big Data in education. This research novelty lies in pointing to Big Data's impact on the architecture of the university educational process in different periods of the COVID-19 pandemic when higher education institutions implemented distance or mixed learning and online communications.

This paper aims to explore and investigate the potential factors influencing students' behavioral intentions to use the e-learning system. This paper proposes an extended technology acceptance model (TAM) that has been tested and examined through the use of both innovation diffusion theory (IDT) and integrating TAM. This paper was conducted on 1286 students utilizing systems of e-learning in Malaysia. The findings were obtained via a quantitative research method. The findings illustrate that six perceptions of innovation characteristics, in particular, have impacts on students' e-learning system behavioral intention. The influences of the relative advantages, observability, trialability, perceived compatibility, complexity, and perceived enjoyment on the perceived ease of use is noteworthy. Moreover, the effects of the relative advantages, complexity, trialability, observability, perceived compatibility, and perceived enjoyment on the perceived usefulness have a strong impact. Therefore, the empirical results provide strong backing to the integrative approach between TAM and IDT. The findings suggest an extended model of TAM with IDT for the acceptance of the e-learning system used to improve the students' learning performance, which can help decision makers in higher education, universities, as well as colleges to evaluate, plan and execute the use of e-learning systems.

In recent years, many studies have emerged about regarding the topic of school failure, showing a growing interest in determining the multiple factors that may influence it [1]. Most of the researches that attempt to solve this issue [2] are focused on determining the factors that most affect the performance of students (dropout and failure) at the different educational levels (basic, middle and higher education) through the use of the large amount of information that current computer equipment allows to store in databases. All these data constitute a real gold mine of valuable information about students. But, identifying and finding useful and hidden information in large databases is a difficult task [3]. A very promising solution to achieve this goal is the use of knowledge mining techniques or data mining in education, which has resulted in so-called Educational Data Mining (EDM) [4]. This new area of research is concerned with the development of methods for exploring data in education, as well as the use of these methods to better understand students and the contexts where they learn [5].

One of the investments of state and private universities to improve the quality of teaching and learning between students and lecturers is the procurement of e-learning system.Due to the large funds of e-learning system investment, the state and private universities need to periodically evaluate whether the lecturers and students have optimally utilized the system.This study aims to find empirical evidence that Unified Acceptance and Use of Technology (UTAUT) model is a conceptual framework to explain the acceptance and use of e-learning system in accounting lecturers of state and private universities in Padang.The sample of this study was 66 lecturers of state and private university in Padang that already implemented e-learning system.The data were obtained by distributing questionnaires directly to accounting lecturers on their respective universities.The data were processed using partial least square (PLS).This study finds the empirical evidence that UTAUT model is able to empirically prove the positive role of performance expectancy, effort expectancy, social influence, and facilitating conditions in improving the intention of accounting lecturers of state and private universities in Padang to use elearning system.However, the model fails to prove the positive role of the intention to use on the actual use of e-learning system.

In the domain of adaptable educational environments, our study is dedicated to achieving three key objectives: forecasting the adaptability of student learning, predicting and evaluating student performance, and employing aspect-based sentiment analysis for nuanced insights into student feedback. Using a systematic approach, we commence with an extensive data preparation phase to ensure data quality, followed by applying efficient data balancing techniques to mitigate biases. By emphasizing higher education or educational data mining, feature extraction methods are used to uncover significant patterns in the data. The basis of our classification method is the robust WideResNeXT architecture, which has been further improved for maximum efficiency by hyperparameter tweaking using the simple Modified Jaya Optimization Method. The recommended WResNeXt-MJ model has emerged as a formidable contender, demonstrating exceptional performance measurements. The model has an average accuracy of 98%, a low log loss of 0.05%, and an extraordinary precision score of 98.4% across all datasets, demonstrating its efficacy in enhancing predictive capacity and accuracy in flexible learning environments. This work presents a comprehensive helpful approach and a contemporary model suitable for flexible learning environments. WResNeXt-MJ’s exceptional performance values underscore its capacity to enhance pupil achievement in global higher education significantly.

In this chapter, the authors propose an overview on the use of learning analytics (LA) and educational data mining (EDM) in addressing issues related to its uses and applications in higher education. They aim to provide meaningful and substantial answers to how both LA and EDM can advance higher education from a large scale, big data educational research perspective. They present various tasks and applications that already exist in the field of EDM and LA in higher education. They categorize them based on their purposes, their uses, and their impact on various stakeholders. They conclude the chapter by critically analyzing various forecasts regarding the impact that EDM will have on future educational setting, especially in light of the current situation that shifted education worldwide into some form of eLearning models. They also discuss and raise issues regarding fundamentals consideration on ethics and privacy in using EDM and LA in higher education.

Higher education instructors constantly rely on educational data to assess and evaluate the behavior of their students and to make informed decisions such as which content to focus on and how to best engage the students with it. Massive open online course (MOOC) platforms may assist in the data-driven instructional process, as they enable access to a wide range of educational data that is gathered automatically and continuously. Successful implementation of a data-driven instruction initiative depends highly on the support and acceptance of the instructors. Yet, our understanding of instructors’ perspectives regarding the process of data-driven instruction, especially with reference to MOOC teaching, is still limited. Hence, this study was set to characterize MOOC instructors’ interest in educational data and their perceived barriers to data use for decision-making. Taking a qualitative approach, data were collected via semi-structured interviews with higher education MOOC instructors from four public universities in Israel. Findings indicated that the instructors showed great interest mostly in data about social interactions between learners and about problems with the MOOC educational resources. The main reported barriers for using educational data for decision-making were lack of customized data, real-time access, data literacy, and institutional support. The results highlight the need to provide MOOC instructors with professional development opportunities for the proper use of educational data for skilled decision-making.